Plasma Cytokine Biomarker Cutoff Values for HIV-Associated Neurocognitive Impairment in Adults

2021 ◽  
Author(s):  
Vurayai Ruhanya ◽  
Graeme B. Jacobs ◽  
Robert H. Paul ◽  
John A. Joska ◽  
Soraya Seedat ◽  
...  
2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Lin Yuan ◽  
An Liu ◽  
Luxin Qiao ◽  
Bo Sheng ◽  
Meng Xu ◽  
...  

Although HAD is now rare due to HAART, the milder forms of HAND persist in HIV-infected patients. HIV-induced systemic and localized inflammation is considered to be one of the mechanisms of HAND. The levels of cytokines in CSF were associated with neurocognitive impairment in HIV infection. However, the changes of cytokines involved in cognition impairment in plasma have not been shown, and their relationships between CSF and plasma require to be addressed. We compared cytokine levels in paired CSF and plasma samples from HIV-infected individuals with or without neurocognitive impairment. Cytokine concentrations were measured by Luminex xMAP. In comparing the expression levels of cytokines in plasma and CSF, IFN-α2, IL-8, IP-10, and MCP-1 were significantly higher in CSF. Eotaxin was significantly higher in plasma, whereas G-CSF showed no difference between plasma and CSF. G-CSF(P=0.0079), IL-8(P=0.0223), IP-10(P=0.0109), and MCP-1(P=0.0497)in CSF showed significant difference between HIV-CI and HIV-NC group, which may indicate their relationship to HIV associated neurocognitive impairment. In addition, G-CSF(P=0.0191)and IP-10(P=0.0377)in plasma were significantly higher in HIV-CI than HIV-NC. The consistent changes of G-CSF and IP-10 in paired plasma and CSF samples might enhance their potential for predicting HAND.


Methodology ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 188-196 ◽  
Author(s):  
Esther T. Beierl ◽  
Markus Bühner ◽  
Moritz Heene

Abstract. Factorial validity is often assessed using confirmatory factor analysis. Model fit is commonly evaluated using the cutoff values for the fit indices proposed by Hu and Bentler (1999) . There is a body of research showing that those cutoff values cannot be generalized. Model fit does not only depend on the severity of misspecification, but also on nuisance parameters, which are independent of the misspecification. Using a simulation study, we demonstrate their influence on measures of model fit. We specified a severe misspecification, omitting a second factor, which signifies factorial invalidity. Measures of model fit showed only small misfit because nuisance parameters, magnitude of factor loadings and a balanced/imbalanced number of indicators per factor, also influenced the degree of misfit. Drawing from our results, we discuss challenges in the assessment of factorial validity.


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